1. Field of the Invention
The present invention relates generally to the field of processor-based imaging, and, more particularly, to region competition via local watershed operators.
2. Description of the Related Art
The segmentation of structures in images is an inherently difficult and important problem. An exemplary but popular application of deformable models is in medical image analysis where the goal is often the extraction of a structure of interest from an image. In the early days of deformable models, the deformation of such models was often guided by differential structures such as edges as well as the internal forces such as elasticity and rigidity. In recent years, it has been proposed that active contours evolving with regional properties of images are less sensitive to the image noise and initial placement of contours. However, region-based deformable models often have difficulties in localizing object boundaries since incorporation of edge term into the evolution is quite difficult. In fact, the quality of results often is not satisfactory enough in many applications; for instance, quantification of medical structures requires very accurate detection of object boundaries.
Like deformable models, watershed transforms have also been very popular in medical image segmentation because they produce closed contours and give good performance at junctions and at places where the object boundaries are diffused. However, typical watershed segmentation produces a large number of regions for even simple images, which is known as the over-segmentation problem. This requires an additional technique, such as nonlinear smoothing filtering, region-growing techniques, and marker methods, to extract the boundaries of an object of interest.
Deformable models have also been used in the extraction of structures of interest on watershed maps. Such extraction requires that the watershed map of an image be computed a priori. However, most watershed methods are designed to operate on the whole image. Recent technological advances in imaging acquisition devices increase the spatial resolution of image data significantly. For example, new multi-detector CT machines can produce images with sizes greater than 1500×512×512. Thus, segmentation method for these data sets need to operate locally in order to be computationally efficient, i.e., rather limited amount of time and memory availability. While cropping data via user-defined region of interest may be solution for well localized pathologies, in many applications, e.g., vascular segmentation or bone removal in computed tomography angiography (“CTA”), the user-selected regions can still be very large. Alternatively, images are sometimes thresholded to reduce the area where the segmentation and visualization method need to operate possibly at the expense of removing anatomically important structures from the data.